New policy ensures fairness in deadline scheduling for disadvantaged groups

Researchers have developed a new outcome-fair Whittle index policy for restless multi-armed bandit (RMAB) problems, specifically addressing stochastic deadline scheduling. This policy aims to ensure fairness for disadvantaged demographic classes by introducing fairness criteria into the reward maximization framework. Numerical examples demonstrate that the outcome-fair policy provides better fairness compared to standard Whittle index policies, though it involves a trade-off between fairness and profit, which diminishes as server capacity increases. AI

IMPACT Introduces a novel fairness-aware algorithm for scheduling problems, potentially impacting resource allocation in systems with diverse user groups.

RANK_REASON Academic paper introducing a new algorithmic policy. [lever_c_demoted from research: ic=1 ai=1.0]

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• outcome-fair Whittle index policy
• restless multi-armed bandit

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